Parallel Image Processing Techniques, Benefits and Limitations

The aim of digital image processing is to improve the quality of image and subsequently to perform features extraction and classification. It is effectively used in computer vision, medical imaging, meteorology, astronomy, remote sensing and other related field. The main problem is that it is generally time consuming process; Parallel Computing provides an efficient and convenient way to address this issue. Main purpose of this review is to provide the comparative study of the existing contributions of implementing parallel image processing applications with their benefits and limitations. Another important aspect of this study is to provide the brief introduction of parallel computing and currently available parallel architecture, tools and techniques used for implementing parallel image processing. The aim is to discuss the problems encountered to implement parallel computing in various image processing applications. In this research we also tried to describe the role of parallel image processing in the field of medical imaging.

[1]  Anne E. Trefethen,et al.  On the Usage of GPUs for Efficient Motion Estimation in Medical Image Sequences , 2011, Int. J. Biomed. Imaging.

[2]  Devrim Akgün.  Performance Evaluations for Parallel Image Filter on Multi - Core Computer using Java Threads , 2013 .

[3]  Thomas Bräunl,et al.  Tutorial in Data Parallel Image Processing , 2001 .

[4]  Rafia Inam.  An Introduction to GPGPU Programming - CUDA Architecture , 2010 .

[5]  Alfred Bork,et al.  Computer-based learning units for many languages and cultures , 2002, International Conference on Computers in Education, 2002. Proceedings..

[6]  John Nickolls.  GPU parallel computing architecture and CUDA programming model , 2007, 2007 IEEE Hot Chips 19 Symposium (HCS).

[7]  Almerico Murli,et al.  Numerical Solution of Diffusion Models in Biomedical Imaging on Multicore Processors , 2011, Int. J. Biomed. Imaging.

[8]  Matthew Brand,et al.  Parallel quadratic programming for image processing , 2011, 2011 18th IEEE International Conference on Image Processing.

[9]  Hui Wang,et al.  Parallel Implementation of Classification Algorithms Based on Cloud Computing Environment , 2012 .

[10]  K. MANJUNATHACHARI,et al.  MODELING AND SIMULATION OF PARALLEL PROCESSING ARCHITECTURE FOR IMAGE PROCESSING , 2007 .

[11]  Versha Rani,et al.  A BRIEF STUDY OF VARIOUS NOISE MODEL AND FILTERING TECHNIQUES , 2013 .

[12]  Kunihiko Kaneko,et al.  PARALLEL IMAGE DATABASE PROCESSING WITH MAPREDUCE AND PERFORMANCE EVALUATION IN PSEUDO DISTRIBUTED MODE , 2012 .

[13]  Jonathan Low.  Medical Image Processing on Intel Parallel Frameworks , 2013 .

[14]  Miklos Kozlovszky,et al.  Parallel biomedical image processing with GPGPUs in cancer research , 2011, 3rd IEEE International Symposium on Logistics and Industrial Informatics.

[15]  Preetinder Kaur.  IMPLEMENTATION OF IMAGE PROCESSING ALGORITHMS ON THE PARALLEL PLATFORM USING MATLAB , 2013 .

[16]  C. Siva Ram Murthy,et al.  Parallel Computers: Architecture and Programming , 2016 .

[17]  Nan Zhang,et al.  Image parallel processing based on GPU , 2010, 2010 2nd International Conference on Advanced Computer Control.

[18]  Jorge de la Calleja,et al.  Point to point processing of digital images using parallel computing , 2012 .

[19]  Martin Schweiger,et al.  GPU-Accelerated Finite Element Method for Modelling Light Transport in Diffuse Optical Tomography , 2011, Int. J. Biomed. Imaging.

[20]  Dan Connors.  Exploring Computer Vision and Image Processing Algorithms in Teaching Parallel Programming , 2013 .

[21]  Hans Knutsson,et al.  True 4D Image Denoising on the GPU , 2011, Int. J. Biomed. Imaging.

[22]  C. Soviany,et al.  Embedding data and task parallelism in image processing applications , 2003 .

[23]  Neeraj Sharma,et al.  Image Processing Tasks using Parallel Computing in Multi core Architecture and its Applications in Medical Imaging , 2013 .

[24]  Neeraj Sharma,et al.  REGION WISE PROCESSING OF AN IMAGE USING MULTITHREADING IN MULTI CORE ENVIRONMENT & ITS APPLICATION IN MEDICAL IMAGING , 2013 .

[25]  Ji-Hoon Kang,et al.  Fast 3D Graphics Rendering Technique with CUDA Parallel Processing , 2014, MUE 2014.

[26]  Piotr Wendykier,et al.  High performance Java software for image processing , 2009 .

[27]  Tomoharu Nagao,et al.  Automatic construction of tree-structural image transformations using genetic programming , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[28]  Alan Edelman,et al.  Interactive Supercomputing’s Star-P Platform , 2006 .

[29]  Antonio Plaza,et al.  Parallel hyperspectral image processing on distributed multicluster systems , 2011 .

[30]  Gerhard Klimeck,et al.  Near real-time parallel image processing using cluster computers , 2003 .

[31]  Emerson Carlos Pedrino,et al.  Automatic Generation of Custom Parallel Processors for Morphological Image Processing , 2014, 2014 IEEE 26th International Symposium on Computer Architecture and High Performance Computing.

[32]  Armando Manduca,et al.  High-performance 3D Compressive Sensing MRI reconstruction , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[33]  Gregory M. Sturgeon,et al.  Patient Specific Dosimetry Phantoms Using Multichannel LDDMM of the Whole Body , 2011, Int. J. Biomed. Imaging.

[34]  Nancy Hitschfeld-Kahler,et al.  A Survey on Parallel Computing and its Applications in Data-Parallel Problems Using GPU Architectures , 2014 .

[35]  Neeraj Sharma,et al.  An intelligent system for segmenting an abdominal image in multi core architecture , 2013, 2013 10th International Conference and Expo on Emerging Technologies for a Smarter World (CEWIT).

[36]  Robert L. Stevenson,et al.  Toolkit for parallel image processing , 1998, Optics & Photonics.

[37]  Zhiyi Yang,et al.  Parallel Image Processing Based on CUDA , 2008, 2008 International Conference on Computer Science and Software Engineering.

[38]  Henk Corporaal,et al.  Skeleton-based automatic parallelization of image processing algorithms for GPUs , 2011, 2011 International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation.

[39]  Gregory G. Slabaugh,et al.  Multicore Image Processing with OpenMP [Applications Corner] , 2010, IEEE Signal Processing Magazine.

[40]  Bahri Haythem,et al.  Image Processing Application on Graphics processors , 2014 .

[41]  Ranajoy Malakar,et al.  A CUDA-enabled Hadoop cluster for fast distributed image processing , 2013, 2013 National Conference on Parallel Computing Technologies (PARCOMPTECH).

[42]  Henning Müller,et al.  Using MapReduce for Large-Scale Medical Image Analysis , 2012, 2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology.

[43]  M Bister,et al.  Biij Biomedical Imaging and Intervention Journal Increasing the Speed of Medical Image Processing in Matlab , 2022 .

[44]  D. Okada,et al.  Digital Image Processing for Medical Applications , 2009 .

[45]  Parimala Thulasiraman,et al.  Mapping Iterative Medical Imaging Algorithm on Cell Accelerator , 2011, Int. J. Biomed. Imaging.

[46]  Neeraj Sharma,et al.  Parallel computation of mutual information in multicore environment & its applications in medical image registration , 2014, 2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom).

[47]  Yan Zhao,et al.  Towards fully user transparent task and data parallel image processing , 2009, 2009 Proceedings of 6th International Symposium on Image and Signal Processing and Analysis.

[48]  B Basavaprasad,et al.  A STUDY ON THE IMPORTANCE OF IMAGE PROCESSING AND ITS APLLICATIONS , 2014 .

[49]  Yasser M. Kadah,et al.  Parallel Computation in Medical Imaging Applications , 2012, Int. J. Biomed. Imaging.

[50]  Silvana Greca,et al.  Multithreading Image Processing in Single-core and Multi-core CPU using Java , 2013 .

[51]  Sandeep Raghuwanshi,et al.  Analysis of Digital Image Processing with Parallel with Overlap Segment Technique , 2013 .

[52]  Cristina Nicolescu,et al.  A Data and Task Parallel Image Processing Environment , 2001, PVM/MPI.